Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset

TL;DR

The paper introduces the Dakshina dataset, a comprehensive resource for 12 South Asian languages featuring native and Latin script text, lexicons, and parallel data, enabling advances in transliteration and language modeling.

Contribution

It presents a new multilingual dataset with native and Latin script data, along with baseline results for transliteration and language modeling tasks.

Findings

Baseline results for transliteration tasks

Demonstrated utility of dataset for language modeling

Resource supports cross-script NLP research

Abstract

This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages. The dataset includes, for each language: 1) native script Wikipedia text; 2) a romanization lexicon; and 3) full sentence parallel data in both a native script of the language and the basic Latin alphabet. We document the methods used for preparation and selection of the Wikipedia text in each language; collection of attested romanizations for sampled lexicons; and manual romanization of held-out sentences from the native script collections. We additionally provide baseline results on several tasks made possible by the dataset, including single word transliteration, full sentence transliteration, and language modeling of native script and romanized text. Keywords: romanization, transliteration, South Asian languages